Self organizing hydrological processes in a runoff source area

CATENA ◽  
2022 ◽  
Vol 211 ◽  
pp. 105955
Author(s):  
Naaran Brindt ◽  
Steven Pacenka ◽  
Brian K. Richards ◽  
Srabani Das ◽  
Anna L. Schatz ◽  
...  
2021 ◽  
Author(s):  
Emma Payne ◽  
Steven Pacenka ◽  
Brian Richards ◽  
Naaran Brindt ◽  
Anna Schatz ◽  
...  

<p>Glyphosate is the most widely used herbicide active ingredient in the world, with 1.35 Tg used globally in 2017. Despite a strong affinity for binding to soil and rapid microbial degradation, in recent studies glyphosate has been detected in agricultural runoff at significant concentrations. This unexplained phenomenon necessitates further study into the mechanism of glyphosate transport from agricultural fields. This study was an investigation into the internal hydrology at a 4.9 ha agricultural catchment and the hydrological processes driving glyphosate transport at this site. Chloride is introduced to this standalone watershed via a point source of sodium chloride road de-icer salt at the top edge of the catchment. The goal of this project was to employ chloride as a tracer to unlock how water moves in the catchment. Since 2015, we have been undertaking annual extensive field sampling campaigns to monitor runoff for glyphosate at an outlet weir. In this project, we used archived samples from the 2018, 2019 and 2020 field campaigns and analyzed over 700 samples for electrical conductivity, over 400 samples for chloride and over 500 samples for glyphosate. During storms, chloride concentration and electrical conductivity decreased as the baseflow component carrying dissolved ions was diluted by the fast response overland flow. During the peak flow of a storm, chloride makes up a consistent fraction of electrical conductivity as the entire catchment contributes to flow at the outlet. We also found that the ratio of glyphosate concentration to electrical conductivity increased linearly with flow rate. The rate of increase (ie., the slope of glyphosate to conductivity ratio versus flow rate) decreases between sequential storms as glyphosate adsorbs and microbially degrades, and from this we extrapolated an empirical degradation half-life of less than 10 days. Cumulatively, the observations of chloride and electrical conductivity suggest that the catchment behaves as a series of connected reservoirs, each with a slow-moving subsurface component and a fast-response overland component. By exploiting the existence of a road salt chloride tracer and soil electrical conductivity in a variable source area, we were able to unlock the hydrological processes at play in areas where surface runoff is generated.</p>


2021 ◽  
Vol 25 (11) ◽  
pp. 5733-5748
Author(s):  
Eunhyung Lee ◽  
Sanghyun Kim

Abstract. Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (at 10 locations with three different depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. The soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, soil moisture difference index, and standard deviation of the peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classifications in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided more insights into the relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events could be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns attributable to combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.


2021 ◽  
Author(s):  
Eunhyung Lee ◽  
Sanghyun Kim

Abstract. Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (in 10 locations with 3 varying depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. Soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, maximum variation, and standard deviation of peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classification in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided greater insight on relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events can be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns made by combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.


1993 ◽  
Author(s):  
Steven A. Harp ◽  
Tariq Samad ◽  
Michael Villano

1998 ◽  
Author(s):  
Svetlana Apenova ◽  
Igor Yevin

1992 ◽  
Author(s):  
Lewis O. Harvey ◽  
Anne Igel ◽  
Eric K. Schmidt

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